Long Intergenic Non-Coding RNAs (lincRNA) Impacts Biology and Clinical Outcome in Multiple Myeloma

RNA has a diverse sets of regulatory functions besides being a messenger between DNA and protein. Recent analysis of RNA repertoire has identified a large numbers of non-coding transcripts. One of which, long intergenic non-coding RNA (lincRNA) with transcripts longer than 200 nucleotides, are located between the protein coding genes and do not overlap exons of either protein-coding or other non-lincRNA genes. lincRNAs have been considered to provide regulatory functions, however, their precise role in cellular biology remains unclear. Here, we have evaluated the lincRNA profile and their clinical role in MM.

We performed RNA-seq on CD138+ MM cells from 320 patients and 18 normal bone marrow plasma cells (NBM) and analyzed for lincRNA. Data from Unstranded 50 bp paired-end RNAseq reads were mapped to the human genome and evaluated for frequency and type of lncRNA. Patient data for MM characteristics, cytogenetic and FISH as well as clinical survival outcomes were also analyzed and correlated with lncRNA data.

We compared differentially expressed lincRNAs and protein coding genes in MM versus NBM samples. lincRNA and protein coding genes that have more than 2 reads/million reads for at least 50 samples (~15%) were included in the analysis. We identified 192 significantly expressed lincRNA (adj p value <0.05). We evaluated neighborhood protein coding genes for lincRNA within 500kb up/down stream and identified 298 genes within the region, 134 of these also differentially expressed between MM and NBM. Gene enrichment analysis to recognize possible biological processes that may be affected by lincRNAs and genes enriched by several Gene Ontology(GO) terms identified DNA binding, transcription, cell proliferation, and regulation of lymphocyte function. We applied unsupervised clustering method to the differentially expressed lincRNA that are neighbor of these 134 protein-coding genes. We identified four distinct clusters which are being investigated for correlation with clinical subtypes of MM. Finally we checked correlation between lincRNAs and clinical outcome including response and relapse free survival. We compared differentially expressed lincRNA between patients achieving complete response (CR) versus others and identified 16 lincRNAs with significantly different expression values (p value < 0.05). Using univariate cox regression model, 26 lincRNAs were identified as having significant correlation (cox p value < 0.05) with event-free survival (EFS). Three of these lincRNAs were also related with response prediction suggesting high level of functional and biological importance. We have developed a multivariate cox regression model utilizing these individually significant lincRNAs able to predict relapse free survival (Overall Wald test p value = 6.736e-07). Using a training set of 171 patients, we developed a cox regression multivariate survival model and created a risk score. The high and low risk based on lincRNA was validated using this model in 85 independent patients (log-rank p = 0.04). We are in the process of now integrating the gene expression data with lincRNA data to develop an integrated survival model.

In summary, we report the first differential lincRNA expression in MM showing a significant role in disease biology as well as clinical outcome. lincRNAs are still functionally poorly characterized and our ongoing integrative approach will provide a link between lincRNAs and protein coding genes in MM.